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Effective Dense Retrieval using Only In-Context Examples
Nour Jedidi, Abdul Basit Ali, Hang Li +1
Turning decoder-only large language models (LLMs) into strong dense retrievers typically requires some form of retriever training. In this paper, we ask whether LLMs can instead be…
A Systematic Study of Pseudo-Relevance Feedback with LLMs
Nour Jedidi, Jimmy Lin
Pseudo-relevance feedback (PRF) methods built on large language models (LLMs) can be organized along two key design dimensions: the feedback source, which is where the feedback tex…
Revisiting Feedback Models for HyDE
Nour Jedidi, Jimmy Lin
Recent approaches that leverage large language models (LLMs) for pseudo-relevance feedback (PRF) have generally not utilized well-established feedback models like Rocchio and RM3 w…
Study on LLMs for Promptagator-Style Dense Retriever Training
Daniel Gwon, Nour Jedidi, Jimmy Lin
Promptagator demonstrated that Large Language Models (LLMs) with few-shot prompts can be used as task-specific query generators for fine-tuning domain-specialized dense retrieval m…
Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
Nour Jedidi, Yung-Sung Chuang, James Glass +1
With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoni…
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Nour Jedidi, Yung-Sung Chuang, Leslie Shing +1
Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Languag…